{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\"../datasets/Google.csv\")\n",
    "df.index = pd.DatetimeIndex(df['Date'].values)\n",
    "close = df[\"Close\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "def get_weights(d, size):\n",
    "    w = [1.]\n",
    "    for k in range(1, size):\n",
    "        w_ = -w[-1] / k * (d - k + 1)\n",
    "        w.append(w_)\n",
    "    w = np.array(w[::-1]).reshape(-1, 1)\n",
    "    return w\n",
    "\n",
    "\n",
    "def plot_weights(d_range, n_plots, size):\n",
    "    w = pd.DataFrame()\n",
    "    for d in np.linspace(d_range[0], d_range[1], n_plots):\n",
    "        w_ = get_weights(d, size)\n",
    "        w_ = pd.DataFrame(w_, index=range(w_.shape[0])[::-1], columns=[d])\n",
    "        w = w.join(w_, how='outer')\n",
    "    ax = w.plot(figsize=(16, 8))\n",
    "    ax.legend(loc='upper right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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a/xsWaOqhCiGEEEKIVuhGM6mm9Oyzz/Lmm29iZnazlwcSrYF8623IhEhvLlXU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dnh7BNO8uT4LQQixdmyFaKYU9aBJjSeP7AfvUKBr+45fItxZTTRVDJbi4gWlLuSKmXb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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3bc3dfe710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_weights([0, 1], 11, size=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8OySExMHBnGpqZXJKJkvyy+g4z0d/NRqFJ64ayNJ7R3GirIGb3vyBA3lV3YpJ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\nKCl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ed9fMve/OvH535t01fTuQaZtkwECTcYNxNliWnHPA2JZUVcq5VCpVPO+P8hy5kAGD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\n4CpJkiRJyjWDqyRJkiQp1/4bTgNR0SU8E88AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3b89588d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_weights([1, 2], 11, size=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def frac_diff(series, d, thres=.1):\n",
    "    # w.shape = (series.shape[0], 1)\n",
    "    w = get_weights(d, series.shape[0])\n",
    "    w_sum = np.cumsum(abs(w))\n",
    "    w_sum /= w_sum[-1]\n",
    "    # Usable only after going over the threshold\n",
    "    skip = w_sum[w_sum > thres].shape[0]\n",
    "    df = {}\n",
    "    for name in series.columns:\n",
    "        series_f = series[[name]].fillna(method='ffill').dropna()\n",
    "        df_ = pd.Series()\n",
    "        for iloc in range(skip, series_f.shape[0]):\n",
    "            loc = series_f.index[iloc]\n",
    "            if not np.isfinite(series.loc[loc, name]):\n",
    "                continue\n",
    "            df_[loc] = np.dot(w[-(iloc + 1):, :].T, series_f.loc[:loc])[0, 0]\n",
    "        df[name] = df_.copy(deep=True)\n",
    "    df = pd.concat(df, axis=1)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def frac_diff_FFD(series, d, thres=1e-5):\n",
    "    w = get_weights_FFD(d, thres)\n",
    "    width = len(w) - 1\n",
    "    df = {}\n",
    "    for name in series.columns:\n",
    "        series_f = series[[name]].fillna(method='ffill').dropna()\n",
    "        df_ = pd.Series()\n",
    "        for iloc1 in range(width, series_f.shape[0]):\n",
    "            loc0 = series_f.index[iloc1 - width]\n",
    "            loc1 = series_f.index[iloc1]\n",
    "            if not np.isfinite(series.loc[loc1, name]):\n",
    "                continue\n",
    "            df_[loc1] = np.dot(w.T, series_f.loc[loc0:loc1])[0, 0]\n",
    "        df[name] = df_.copy(deep=True)\n",
    "    df = pd.concat(df, axis=1)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tomoaki/anaconda3/lib/python3.6/site-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
      "  from pandas.core import datetools\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(-2.4310671816546217,\n",
       " 0.13315919893986067,\n",
       " 1,\n",
       " 3123,\n",
       " {'1%': -3.432445639802861,\n",
       "  '10%': -2.567262891276225,\n",
       "  '5%': -2.8624659237441654},\n",
       " 25325.977157525936)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from statsmodels.tsa.stattools import adfuller\n",
    "\n",
    "adfuller(close, 12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "outputs = []\n",
    "ds = np.linspace(0, 1, 11)\n",
    "for d in ds:\n",
    "    df1 = np.log(df[['Close']]).resample('1D').last()\n",
    "    df2 = frac_diff(df1, d, thres=.1)\n",
    "    df2 = adfuller(df2['Close'], maxlag = 1,regression = 'c',autolag = None)\n",
    "    # Pvalue\n",
    "    outputs.append(df2[1])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f3b749275f8>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3b750fde48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(ds, outputs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f3b74900cf8>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SVYhR8IeEzbO/PecJ71sMVKYYX7CtQI29BH64IaYYXzhfBpwlrFAVCVquLodS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\nMaxgt1gslhrDCnaLxWKpMf4/bVe0VJz01loAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3b749369e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.random.randn(1000)\n",
    "plt.plot(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(-32.8247093405595, 0.0, 0, 999, {'1%': -3.4369127451400474, '5%': -2.864437475834273, '10%': -2.568312754566378}, 2724.3632887028107)\n",
      "p-value:  0.0\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.tsa.stattools import adfuller\n",
    "\n",
    "\n",
    "res = adfuller(x)\n",
    "print(res)\n",
    "\n",
    "p_val = res[1]\n",
    "print(\"p-value: \", p_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(-1.0841988393323927, 0.7213319164463725, 0, 999, {'1%': -3.4369127451400474, '5%': -2.864437475834273, '10%': -2.568312754566378}, 2724.6914123735705)\n",
      "p-value:  0.7213319164463725\n"
     ]
    },
    {
     "data": {
      "image/png": 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EQKMJwd/Z3I3bXtiMU8RY42EledjU0AWfn0csJ5upOILqmn+w8zhOrSmDM2gy\nVnLvbDiktryI5HEFfdb14gTuzYvG4fwp1TEnMQ0GUty8kkTCRm85a7y8rMztUP6cKAw4+9DEX8uo\n1+FAmw0vfSn0E91zXIjQeanuaLSXZSTKRhZmg072zzsjuKi2Hs298NN0c1J1+EnNygJzRos9AMwf\nX6Hy2QPqAmmJoBT1AoULMdpTdI9TaLeprPFDDD6aEPyyCJNm2ZgcohT8806uxoE2wbp0RRB8m9sH\nk0GHO86dIIflEcnxh69Px9+C3BX5Jj3Om5wdLSNPHqaemO0vDLM/lk0dhlvPGo/bFtfimRsC37H2\nPheW/Hktvjgc2iT92sc3AAAeXXsgqfcmUosmBD+4eNXMUUIJ2fd2HMu6BJE/fbgXAPDotbMwtiIf\n7X1uPPLxgaiT0BOHFOK2xbWYH5RxSSRGgdmA805Wi/vC2grossQ9mG8OzPdUFppxWVCTlXgxGXS4\nc8lE3HHuBEweVoRHrpkFQEhu3NvSi1++tVN1/OaGTmwVn0yD6/wQg4smBD+4OuSzNwpWyI6mHtz+\n4pbBGFLCfFYv+OSriyyoECdm739vD1bvbo34mkUTKwdkbLlEcKRKaZTm4pmGVAHz2nmjsf6exSmv\nnloouoxe/KIBAOS6+RIf7Ql8V6kvQ2aRdsFnjC1ljO1ljNUzxpan+/1W3XGGys/YnEX9OJWZwhOr\nC1FZEBCZDYdCH5slJg+NnkhD5BZSpNfMUSVpCVqQmrBsEsuXuH3qSW4pggyI3EiGGBzSKviMMT2A\nvwM4H8BkAFcyxmLr9JAgw4LqfmeTZeYUS/IuP38SLEY9FoyP7qKRapVXFWVGpqeWOdppH+whxMxX\npg8DANRWxRd/Hyv5ZoOqHWRwVnufor9ucCXPWNjU0EklmNNEui38OQDqOecHOeduAC8CuDidbyjF\nq/9k6SQA6mSsTEeasLWahGsI7m8q9RyVePbGOfjmaaMxdXj/2ZNE/LzyvdPwm0um4PTaCvn7lA18\n49RRWLf87JiyahNlSJCR4VVY+X2uQJKaQRe/xFz68Dpc+LdPEx8cEZF0C/5wAMrYyEZxmwxj7CbG\nWB1jrK6trS3xNxIte8ln+L1F43DDgjE41uXMisdKpyfQKzVc9ua65WfjomnDVNsmDCnEry6eQrHQ\naWLW6DJcM280nr1xLqaNKBns4cRF8JNuqgkuGKfME7G5fCiyGPCjcyZga2MX/rvzON7fcTym8yqL\n/2XD7zYf/3M/AAAcfElEQVTbGPTEK875YwAeA4DZs2cn/Bd+57aF6LKr6+cMLbbA4fGhx+FNuu9o\nurnvnd14dv0RAAELHxDcO0c77PIPeP09i1Hf2ofDJ2wZl9ZP5A6GoLkBp8cnz531ODwotBhx7uQh\n+POqffiuWGUzuOhfOG58+gt5ucfpld2WRGpIt+A3ARipWB8hbks5JVaTqicpALn+t82deYLPOccz\nnx/BxTOGocRqwp7jPfI+peDffOY41euqiy2oLrZgYS2FYBKDh0cMMJDKTSgTA1t6nRhSZMZJQ9Vz\nCO19rqiJa16fH5/sb5fXO2xuEvwUk25fwBcAahljYxhjJgBXAHgzze8pI5W4jZSlOpjsbO7Bz9/c\niTtf2goAGFlmlfclUvuEIAYSyWc/ulz43iqbvh/rdqK62BISktnaE5oT89jaA/jhi0J/YKnarZQZ\n3GHLrhyabCCtgs859wK4FcB/AewGsJJzvjP6q1KHRYylVn4ZM43GTiFsVNkXVSpaRRCZiuRrl9w4\nklHFOcfxbqc8qbts6lD5NeFKlv/23T14Y4tQfkGqD3WNWIWTau6nnrTP9nHO3+WcT+Ccj+Oc35fu\n91Mi+bhd3syx8J0eH1p6nPCKj8TK7kQSkUpFEESmcNviWswcVYKLxBBQ6TfW6/LC7vbJFTZXXDkT\nK797mrDPGbn2jsvrk7toST0gOrKwNEqmo+nwDrPs0skcC//Xb+/C3N+uRpNo2Td3O/HRnhY0dAhx\n3nctmUjZiUTGM7ayAK99f4GcZCX9xlrE5iuSha/XMVSICYS9rshNiVp7XLLgS/X7s7EWVqYz6FE6\n6USy8P+3rxWnjSsf5NEIfFYvTEqtVzSUuOEpoTn2kCKzqiwtQWQ6eWKAwdvbjqG8wKQqDSIh5ZO0\n97rR6/Qgz6gPKffQ7fCgtVe4WYwoFeYFwhVlI5JD04Iv1fF+9OODuOu8iSmvKZII0pdfirlXYsyA\n8RFEPEjulx6nB0v/8om8fWhxIA9Aitm/793duO/d3QCA3b9aKt8sAMG12dbrQqHFEDDU9iael0OE\nR9MKo4xT7w7jKx9IhD60fjlyqO5IZ8gxJPhEtmE26DFjZAm2N6pLISj74ob7Xp/0s/fR2hPovdvt\n8KDH6ZVvDlecKkRz+/yJpeZ8Vt+OKx77XJXIRWhc8MsVk59dYQS/w+bG1/6xDg0n0l8n5fmNDai9\n9z0c7YhczO1QO7UrJLKP4jyjPAcFCKGasSQFzvntanm5x+mB3e2F1Sg4HaRWjX0Jtiq9+vENWH+w\nA81d2VM8cSDQtOCXWE24e+lEAEBXmJo6b2xpwpdHOnHGg2uwdl+bqlplqnlZ7MZ1XGHVEIQWCLbg\np4cpQ3HNvFFRz9HjEKJ7JDePFO6ZqOBLLPrD/5J6vdbQtOADwAKxKUhw2QVA3aLtm09slEU5HYS7\nmdx0xlgsPbk6be9JEAOBsuEKEMhwV/KbS6ZGPUeP0wOH2xdSODBc7D6ROJoXfMkn2BlO8IMahu9v\n7U3bOLxhBL/UasJVc6NbPgSR6fzqK1NU68p+FEqk8juSf15Jj8MDu0Lwi/KEc7y1tRlen19VjZNI\nnBwQfMGPH86lI1n4F0ythlHP0hrFE67VYlm+UZVkNUpRXoEgsoViq1FVultZC0rJG7csxN1LJ+KX\nF58sb7tryUSMLMtDj9MLh8eHPJMg9KfWlAEA+pxefPXhdbjooc/SeAW5g+YFv9BsgI6Fd+n0ubwY\nXpKHh6+ehTyjHg53ejJy39zajJYwdURKrSaUi0kpRj3DmjsXpeX9CSLdXDxjOK6fXwMAyDeFt/Cn\njijG9xeNh9mgx/77zsdLN5+G7505Dq09Lry2uUmctBVuFhajHkOKzHB6/Nje1I3dx3pijthJ51xc\ntqN5wdfpGEqtJjz+6cGQImp2l0/2P1pNBpVPP5W8v+NY2O0FZoNs4Rt0urS0oyOIgcIjul0sESx8\nJUa9DqfWlEGnY3CJoZMtPS5VbL7ZoFdl2/ZFKc2gJJuaHg00mhd8ALh+fg2cHj++80ydKgTT5vYi\nX/Q3Ws162NNUVdPl8ctJYABww4IxAITQM7NBjzvPm4CXbj4tLe9NEAOFNF9WHmctqP+7MND1VOkO\nshh1WLW7RV7viXECNzgSLp5IH6fHp+knhJwQfKl2/Cf723HGg2uwapfwJepzeeXHT6spPS6dP36w\nF6v3tMo+SQC49rTROHz/MrkX7a1n12IKtSkkspzr5tfgwcumxR15NrwkUIZBKfjBSVOxCn6LKPhS\nF7w9YbLaw+H1+THp/97Hb8VsYC2SE4JfGBQm9u1n6sA5R4fNLVsl+SZDzI+M8fC3j+qF85v1+MHZ\nQp0cqZIgQWiJqkILLp89Ero4XZP5iqiePIX/P9jQ7nHE9vuUsuqlJ4dw82fhkNxHT647HNPx2UhO\nCH6BObShyIL7P8KRE3Y5Mqai0Bw2kiZZpO++Ua/Dj86ZgD2/XkqtCQlCgVLwlRZ+8CTtlf9cH9P5\n7OKTemWh4FqyuaPfKJweH3Y19+C4WOlTy1NpuSH4YRJBmsU/bk250GykssAsl2dNJVKpY6NeB52O\nkdgTRBAFMQo+EFtjc8k1K7VTtPfjw7/zpa24YMUn2Nci5OHoNFyePCcE36oQ2b2/WaraJ/nOq4st\n6HV5UbP8nZS+t/SlDW76TBCEgMqlo/it+kRxv21xrbwtFreOPUjwgxMsg/l4n1CVc89xQfC13IAo\nJwRfp2N44vrZ2PD/FsNs0OMXFwWiAiYPKwIALJpYKW9LR4csoyEnPmqCiJt8hVWvfBovEpcXKHpZ\nNHcLxdA45zjQ1he2X7Xd7YNJr4PVpIdex2Dvx6XjEpu37GoWJne1/BSeMyp09qQhcheek4YKIj9M\nMXkqNV0AgE5b6up3TB8hPEF894yxKTsnQWgJpYVfZAnMt51z0hAA6pvA6t0t+GhPC6b98gMs/uPH\nuGPllpDzOdxe5Jn0YIzBatLLFn84NjV0wi3mD3wuNiVypSk8OxPIGcFXIpVbcCvqcyitjBO21Pny\nrSYD5tSUYXQ5NSYniHAoq20qI+ruXjoJK797Gk4eViz7+dv73HhjS7PcH3f17taQ89kUNXnyTQbY\nXZEF/Icvbg7ZJrUd1SI5KviCFaGM81X2kU1l82Sn1yf31iUIIjpKC1+vY5gzRshf2fHLJRhabMG2\nxi7V8eEmdlt7XbL/3mrWR43SidSf4i+r9sc99mwgJ5WoOE/4Us0YVRp2f7DgH2zrwx8/2BtThEAw\nQpatdn2CBJFKwkXUSRzrdmJTQ5fsawcCE7tKmjrtGFEqJF3lmwwRXTrhfs+zRwuaYNbonJs2r6of\nLEY93rx1Af5+1UzV9k3/dy4AoCmoS851T27E3z6qTyhs0+b2osBMgk8Q0bhs1gicPakqpjafyt9n\nsGa7vD4c7XTI+TVWkz5ijSxXUCbvg5dNk5e12m5U003MozEtTFeeEtHyf+D9vfj+IiErdsPBE/Jj\nXyIVNvqc3qhWC0EQwB8unx7zsUqLPbj2/t7jvXB7/ZgxUvh9W016tPeFd9FK8fp3LZmI7505Djod\nw4tfHI136FmFNm9jCRIuJfze13fIy6c/sAZHO+Lrf9vr8obN9CUIIj5+raij/5Xpw/Cd08eE+PCl\nOP2KQsmHbwjrw3d6fLjzpa3CsQUm+bfvFx8ZtJp7RYIfxI/OmQBAKPX6wsYG1Lf2yfvcXj+e29AQ\n87lcXh/cXn9ILR+CIOJntqIA4Q0Lx6A03wSHx6cqeijl0JhEl0x+BJfOugPtWL1HiPBR1u85a2IV\nAKEukBYhwQ+iNF+wxrsdHvwnzONdXhxJGTYxHCxSyzeCIGJH+TsqyTOiVOpm5wi4bKTIOykyrjTf\nhE6bJ6Tkca+iUKIyE//WswRXbrwlnrMFEvwgpAieLrsbFQWhf3RLHCGWUvVNEnyCSB4pnFpaln6r\nUnVMIDARK1n4I0utcPv8aOlV18g/1h1YV9bv0ekYiiyGsD2otQAJfhCy1WD3wKAL/XjW7A1N9IhE\nr0v4ItKkLUEkT6ElIPLFeUY5Zr+jL5yFL4i4FJ7ZHBR599yGIwCEJkTjqwpU+0wGndy9S2uQ4Ach\nWRGddg8cHh+mjSjGa9+fL+9ff7Aj6uvf2NKE+b9bDZ+fyxZ+IVn4BJESPrzjDHz503PAGENRnvC7\nuurxDfL+YB++VAhNWS7leLdTjrxbdceZciMiCYNOB6+PLPycIGDhu+H0+GAx6kNicqO1QFv+ynY0\ndzvR7fDIrdXIwieI1FBVaEG5mEVbqMjKvf7JjVh3oF126cg+fPH3/MGu4/Kx/bU8NBoYWfjhYIxd\nzhjbyRjzM8ZmB+27hzFWzxjbyxhbktwwB45iq+TD98Dp9cNi1IdUz+xy9F9crcvuDgg+WfgEkXJq\nyq1YcrJQYO1/e9tw1T83hPjwpd/zyrpG+XVSVM/9l04Ne16jTgdPkFH3WX27qh92tpKshb8DwKUA\n1io3MsYmA7gCwMkAlgJ4mDGWFemmhWYD9DqG/9QdxdajXdAxYMbIUnx/0Tj8TG6Z5oz4eil+t73P\njcZO4bGRLHyCSD2MMTxyzSzVNqksilQaQelOlUopSOWSR5ZZEQ6DnsGrsPA557j68Q1Y+te1YY/P\nJpISfM75bs753jC7LgbwIufcxTk/BKAewJxk3mugkEqqSvH3nx84Ab2O4e6lk+RyrevFMqrR+Pqj\nn+PB/+5FZaEZleIjKEEQqYUxhoumD5PX97X0wqTXycUQGWO4cs4oAMJTOyBY6wCQZwpvgxr1wqSt\n38/xnWfq8GMxQcvu9iVUTyuTSJcPfzgAZRB7o7gtK1DG6P5Kkd03qtyK4jwjDrbZQl7z23d3Y9Wu\nlrDdcphW0/YIIgO4/9Kp+MfVpwAAPtnfjrGV6lLk88YKCVsnbC502z1Y8VE9AKGwWjjMBh2cHj/a\n+lz4cFcLXt3UJO9bWZfdpRf69TUwxlYBqA6z617O+RvJDoAxdhOAmwBg1KhRyZ4upey/7/yQCdtu\nhwfPrj+Cn100Wd73h//uxWNrD+KxtQdVTVWA0OJOBEGklnyzAbNGByrfThObDklIme59Lh/+u/OI\nvN2oD2+IFeUZsb+lD/8LE4J9OMv9+P1a+JzzczjnU8L8iyb2TQBGKtZHiNvCnf8xzvlszvnsysrK\ncIcMON+YPRJzxpSFrZgnfXkk/zwAPLSmXl5u7lb798dVUuMTgkg3VUUWnD1JKItgDbLcpVpWX3/0\nc1WS1dDivLDnKs4zoqnLgZ+8sj1kn9ub3dE76XLpvAngCsaYmTE2BkAtgI1peq+U8/vLpmHld08L\nu2/FFUJJ5WPdDnh9fnTbPREblD/+zdn4R9CkEkEQ6eHMCYLBGFz2WIqSc3v9eHvbMQBA3U/PiejD\nl5K7gjHqWVr6XQ8kSYWPMMa+CuBvACoBvMMY28I5X8I538kYWwlgFwAvgFs459n9SYmUi+UWrvrn\nBiw9uRp3LpkYkob9k6WTcNH0oao+uQRBpBdJwINFWVm8sEGsdmuNIPYAoDTfiiwG9IhzehUFZrnh\nebaSbJTOa5zzEZxzM+d8COd8iWLffZzzcZzziZzz95IfamYgJXIAwPs7j8MZ1PB4Tk0Zbj5zLIk9\nQQwwUihmsCgrxV1qYmSJ0oXutHEV8vK9y04CAHx15nBYjHpVH+xshALE40TZc3NIkTnk8bGpy0FR\nOQQxCNRWFQIAThmtbl1aXmDGI9fMwi3Pb5Lr54frfSGxdEo13r/9dBRZjBhWkocF4yswvCQP5//1\nk9y28HMRpd+vpceFxz85CAByS7X+0rYJgkgPk4cVYe1dZ+GGBTUh+5ZOqcbo8tifuidVF2FYiTCp\nO6LUCsYYTAZdiLvoxY0NOP2Bj5Ia90BCFn6cmIKaG7+3Q6jR8fuvTcOV/1yPMRUUlUMQg8WoKKIe\naTI2VswGHdbsbUO33YOmLgdufX4TDrYLOTkurw/mKG6iTIEEP0WUWI34941zMWlo4WAPhSCIMEj1\ndX5w9viEXr+9qRsA8MB/98Dm8spiDwjNjrJB8MmlkyIsRj0W1laggsooEERGIuXVDC8JH3/fHyPF\nQAybyyuHd0r0OvsvqBiNPpd3QMo2kOCnCLOBPkqCyGSk36g5jq51Sh69VsipeX1Lc0gotrIcSyJc\n+vBn+P5zm5I6RyyQSqUIEnyCyGykJuiJNjcZW1mASdXhXbbJBmu09rrkHJ90Qj78BPjk7rPQ0uPE\nZY98Lm+LlLVHEERm8J3Tx6A834RLZiZex3HaiGLsOd4bsj0ZC9/l9aHL7kFVoaX/g5OEBD8BRpZZ\nkR/U1CTPSIJPEJmMQa/D108d2f+BUVD2vx1ekocmsVdunytxH/66eqHcelVh+uf/yA+RIMGp2ZRs\nRRDaR4rEOWloEUrzA2GefQla+H4/x7ee+gIAMLo8/SHdZOEnCPnsCSL3sIgTviY9U1Xl7ElQ8D9X\nNFOaPKwoucHFAKlWgjDGcNLQ9P+BCILIHCQL36DX4Tyxny6Q+KStVMztpZtPSzoxLBZI8JPgyetP\nBRDoY0sQhLaRnuwNOoZr5o2Wtycahy81VK9VzA2kExL8JBhSZMYvv3Iy3rhlwWAPhSCIAUCK4Tfq\ndTDqdbLRJ/XLjReHWG3XMkBBH+TDTwLGGK6bXzPYwyAIYoBgYrV8vVht86xJVThlVAk67W64vX60\n9jrjKo3u8vjA2MDNCZKFTxAEESMesR6+sv1pWb4JnTYPlr+yDQt/vyakR0Y0HB4f8oz6AYvyI8En\nCIKIEamkgrIBeonVhE67G//dKVTOlfzysSAJ/kBBgk8QBBEjU4cXAwC+dsoIeVuB2YA+l1e+Gdjj\nsfDd/gHz3wPkwycIgoiZkWVWHL5/mWpbvlmvsurjsfDtbq8c2z8QkOATBEEkgdVkUFXPjEfwD7T1\nDUiGrQS5dAiCIJIgP6jMit0dWxKW389xoM0WsQJnOiDBJwiCSAJliQUgdh9+l8MDn5+jcgCKpkmQ\n4BMEQSSB1ay28J0xunRO9LkACGGdAwUJPkEQRBIUBJVKt8co+O19bgBAeT5Z+ARBEFlBsGDH6tLZ\ncrQLAFA7ZGDq6AAk+ARBEElRFtSaMFaXzr6WXgwvycOQovR3upIgwScIgkiC8iAffCwunfY+F+pb\n+1BVNHDuHIAEnyAIIimUmbImvQ52jzos0+/n+PkbO7DneI+8bfZvVmF7UzeOdTkHbJwACT5BEETS\n/O/ORXj3ttNhNetll47fz/Hyl41o7nbg6c+PYOlfPgl53RJFE5WBgDJtCYIgkqSmQsiWzTPqZZfO\na5ubcOdLW3F6bYXqWM4DWbk/v+jkgRskyMInCIJIGcV5RnTaPXB7/fjxS1sBAJ/sb1cdI90Qlp8/\nCTrdwLbLS0rwGWMPMsb2MMa2McZeY4yVKPbdwxirZ4ztZYwtSX6oBEEQmc2QIgtaepyYf//qiMd0\n2IT4+4FMuJJI1sL/EMAUzvk0APsA3AMAjLHJAK4AcDKApQAeZowNXA1QgiCIQWBosQXHup1yUlUw\ns379IY6KjcvLrFkm+JzzDzjn0pT0egBSkeiLAbzIOXdxzg8BqAcwJ5n3IgiCyHTK8oVmKJE4YXPj\n0bUHAQClWWjhK7kBwHvi8nAARxX7GsVtBEEQmqUozwifWCq50GLASUOLQo5xeQUffka6dBhjqxhj\nO8L8u1hxzL0AvACei3cAjLGbGGN1jLG6tra2eF9OEASRMRRZjPLydafV4LJZI0KO2Xu8FwBQUTDw\ngt9vWCbn/Jxo+xlj1wO4EMBiHog3agIwUnHYCHFbuPM/BuAxAJg9ezYPdwxBEEQ2UGgJSGqJ1Ygb\nF45BqdUIo16HH7ywGQDQafeIxxrDniOdJBulsxTA3QC+wjm3K3a9CeAKxpiZMTYGQC2Ajcm8F0EQ\nRKZTlBcQ8WJx+dJTRuCi6cOw/RfnDdawZJJNvHoIgBnAh4wxAFjPOb+Zc76TMbYSwC4Irp5bOOex\n9/0iCILIQtQWviloX+BmsObORQM1JBVJCT7nfHyUffcBuC+Z8xMEQWQTSh9+cV5kl031AFbIVEKZ\ntgRBECmiKE/tw4+ExTg40kuCTxAEkSKUFn5JFAtfdIEPOFQ8jSAIIkWYDQEbuiiM4N+9dCI2N3QN\n5JBUkOATBEGkCKXlrqyTL/H9RRGnPQcEcukQBEHkCGThEwRBpJDnvz0XjV2OwR5GWEjwCYIgUsj8\n8RX9HzRIkEuHIAgiRyDBJwiCyBFI8AmCIHIEEnyCIIgcgQSfIAgiRyDBJwiCyBFI8AmCIHIEEnyC\nIIgcgQW6Eg4+jLE2AEeSOEUFgPYUDScbyLXrBeiacwW65vgYzTmv7O+gjBL8ZGGM1XHOZw/2OAaK\nXLtegK45V6BrTg/k0iEIgsgRSPAJgiByBK0J/mODPYABJteuF6BrzhXomtOApnz4BEEQRGS0ZuET\nBEEQEdCE4DPGljLG9jLG6hljywd7PKmCMTaSMbaGMbaLMbaTMfZDcXsZY+xDxth+8f9SxWvuET+H\nvYyxJYM3+sRhjOkZY5sZY2+L65q+XgBgjJUwxl5mjO1hjO1mjJ2m5etmjP1I/E7vYIy9wBizaPF6\nGWNPMMZaGWM7FNvivk7G2CzG2HZx3wqWaBd0znlW/wOgB3AAwFgAJgBbAUwe7HGl6NqGAjhFXC4E\nsA/AZAAPAFgubl8O4Pfi8mTx+s0Axoifi36wryOB674DwPMA3hbXNX294rU8DeDb4rIJQIlWrxvA\ncACHAOSJ6ysBXK/F6wVwBoBTAOxQbIv7OgFsBDAPAAPwHoDzExmPFiz8OQDqOecHOeduAC8CuHiQ\nx5QSOOfHOOebxOVeALsh/FguhiAQEP+/RFy+GMCLnHMX5/wQgHoIn0/WwBgbAWAZgMcVmzV7vQDA\nGCuGIAz/AgDOuZtz3gVtX7cBQB5jzADACqAZGrxezvlaAB1Bm+O6TsbYUABFnPP1XFD/ZxSviQst\nCP5wAEcV643iNk3BGKsBMBPABgBDOOfHxF3HAQwRl7XwWfwFwN0A/IptWr5eQLDm2gA8KbqyHmeM\n5UOj1805bwLwBwANAI4B6OacfwCNXm8Y4r3O4eJy8Pa40YLgax7GWAGAVwDczjnvUe4T7/iaCLVi\njF0IoJVz/mWkY7R0vQoMEB77/8E5nwnABuFRX0ZL1y36rC+GcKMbBiCfMXaN8hgtXW80Bvo6tSD4\nTQBGKtZHiNs0AWPMCEHsn+OcvypubhEf8yD+3ypuz/bPYgGArzDGDkNwzZ3NGPs3tHu9Eo0AGjnn\nG8T1lyHcALR63ecAOMQ5b+OcewC8CmA+tHu9wcR7nU3icvD2uNGC4H8BoJYxNoYxZgJwBYA3B3lM\nKUGcif8XgN2c8z8pdr0J4Dpx+ToAbyi2X8EYMzPGxgCohTDZkxVwzu/hnI/gnNdA+Dt+xDm/Bhq9\nXgnO+XEARxljE8VNiwHsgnavuwHAPMaYVfyOL4YwP6XV6w0mrusU3T89jLF54uf1TcVr4mOwZ7FT\nNBN+AYQIlgMA7h3s8aTwuhZCeNzbBmCL+O8CAOUAVgPYD2AVgDLFa+4VP4e9SHAmPxP+AViEQJRO\nLlzvDAB14t/6dQClWr5uAL8EsAfADgDPQohM0dz1AngBwjyFB8KT3I2JXCeA2eJndQDAQxCTZuP9\nR5m2BEEQOYIWXDoEQRBEDJDgEwRB5Agk+ARBEDkCCT5BEESOQIJPEASRI5DgEwRB5Agk+ARBEDkC\nCT5BEESO8P8BNADKvTGokk0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3b74980630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_sum = x.cumsum()\n",
    "plt.plot(x_sum)\n",
    "res = adfuller(x_sum)\n",
    "print(res)\n",
    "\n",
    "p_val = res[1]\n",
    "print(\"p-value: \", p_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(-13.251308027880501, 8.764632670605858e-25, 18, 979, {'1%': -3.4370471695043037, '5%': -2.8644967585264784, '10%': -2.5683443301882956}, 2779.4928640901026)\n",
      "p-value:  8.764632670605858e-25\n"
     ]
    },
    {
     "data": {
      "image/png": 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gMs9hA2e5V/ixJiQsiLbm85rwmmIcyEM/cr24RfaV1HtTjMcF2uQJg23MiOxA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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3b7482b668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "over_diff = np.diff(np.diff(x_sum))\n",
    "plt.plot(over_diff)\n",
    "res = adfuller(over_diff)\n",
    "print(res)\n",
    "\n",
    "p_val = res[1]\n",
    "print(\"p-value: \", p_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f3b74737940>]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hHEeqXPUFLTxPTnFuCs6GTuo7tM5wI/UdPZxr6KTIT/uXT8YENsbswPXHf/hr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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3b748420b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(0, 4*np.pi, 1000)\n",
    "y = np.sin(x)\n",
    "plt.plot(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(-60578028420846.48, 0.0, 13, 986, {'1%': -3.4369994990319355, '5%': -2.8644757356011743, '10%': -2.5683331327427803}, -66468.98189429524)\n",
      "p-value:  0.0\n"
     ]
    }
   ],
   "source": [
    "res = adfuller(y)\n",
    "print(res)\n",
    "\n",
    "p_val = res[1]\n",
    "print(\"p-value: \", p_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5.4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_weights_FFD(d, thres, size=10000):\n",
    "    w = [1.]\n",
    "    for k in range(1, size):\n",
    "        w_ = -w[-1] / k * (d - k + 1)\n",
    "        if abs(w_) <= thres:\n",
    "            break\n",
    "        w.append(w_)\n",
    "    w = np.array(w[::-1]).reshape(-1, 1)\n",
    "    return w\n",
    "\n",
    "def frac_diff_FFD(series, d, thres=1e-5):\n",
    "    w = get_weights_FFD(d, thres)\n",
    "    width = len(w) - 1\n",
    "    df = {}\n",
    "    for name in series.columns:\n",
    "        series_f = series[[name]].fillna(method='ffill').dropna()\n",
    "        df_ = pd.Series()\n",
    "        for iloc1 in range(width, series_f.shape[0]):\n",
    "            loc0 = series_f.index[iloc1 - width]\n",
    "            loc1 = series_f.index[iloc1]\n",
    "            if not np.isfinite(series.loc[loc1, name]):\n",
    "                continue\n",
    "            df_[loc1] = np.dot(w.T, series_f.loc[loc0:loc1])[0, 0]\n",
    "        df[name] = df_.copy(deep=True)\n",
    "    df = pd.concat(df, axis=1)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.random.randn(df.shape[0])\n",
    "dummy = pd.DataFrame({'Close': x.cumsum()}, index=df.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3b7477ba58>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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klV4GPD39p8f2jLh3+vO+bfDYmPMiuk9/1MnFbv3a5uLlm/q6li/67cUgAjbK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\n//y4D+p7go0lghkm4lgshAcv64IcHf2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,
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3b74960240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dummy.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2008-04-24</th>\n",
       "      <td>0.341830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-04-25</th>\n",
       "      <td>0.080560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-04-28</th>\n",
       "      <td>1.366998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-04-29</th>\n",
       "      <td>0.833227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-04-30</th>\n",
       "      <td>2.273546</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Close\n",
       "2008-04-24  0.341830\n",
       "2008-04-25  0.080560\n",
       "2008-04-28  1.366998\n",
       "2008-04-29  0.833227\n",
       "2008-04-30  2.273546"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frac_df = frac_diff_FFD(dummy, .5)\n",
    "frac_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Close]\n",
       "Index: []"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frac_df = frac_diff_FFD(frac_diff_FFD(dummy, 1), -1)\n",
    "frac_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0 1.0\n",
      "0.2 nan\n",
      "0.4 0.7494767651866965\n",
      "0.6000000000000001 0.37385762818095\n",
      "0.8 0.14839962978101104\n",
      "1.0 0.049702139183430756\n",
      "1.2000000000000002 0.01173853807929471\n",
      "1.4000000000000001 0.0014916464558962764\n",
      "1.6 -0.0009347472756766786\n",
      "1.8 -0.0012492743274609918\n",
      "2.0 -0.00013139183013107452\n"
     ]
    }
   ],
   "source": [
    "corrs = []\n",
    "ds = []\n",
    "for d in np.linspace(0, 2, 11):\n",
    "    frac_df = frac_diff_FFD(dummy, d)\n",
    "    close_frac = frac_df[\"Close\"]\n",
    "    corr = close_frac.corr(dummy[\"Close\"])\n",
    "    print(d, corr)\n",
    "    if np.isfinite(corr):\n",
    "        corrs.append(corr)\n",
    "        ds.append(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f3b748b0278>]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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K9+r5z1Xve12OiERJ4S7duvacQi4+Yxg/f36bfhopkiIU7tItM+PfvzCRnKwgty3dQEub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\n+v+FGpkhYoscOgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3b746389e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(ds, corrs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tomoaki/anaconda3/lib/python3.6/site-packages/statsmodels/tsa/stattools.py:1018: UserWarning: y0 and y1 are perfectly colinear.  Cointegration test is not reliable in this case.\n",
      "  warnings.warn(\"y0 and y1 are perfectly colinear.  Cointegration test \"\n"
     ]
    }
   ],
   "source": [
    "import statsmodels\n",
    "\n",
    "ps = []\n",
    "ds = []\n",
    "for d in np.linspace(0, 2, 11):\n",
    "    frac_df = frac_diff_FFD(dummy, d)\n",
    "    close_frac = frac_df[\"Close\"]\n",
    "    close_ = dummy[\"Close\"].loc[close_frac.index]\n",
    "    if len(close_) > 0:\n",
    "        # Coefficient of the first argument will change\n",
    "        res = statsmodels.tsa.stattools.coint(close_frac, close_)\n",
    "        ps.append(res[1])\n",
    "        ds.append(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f3b7485f860>]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WjcREJB4K9wqZPqWRB5fNpbv3JO5amhGR6lK4V9CGIMdnpy5zUI3ERKTKFO4VdLORmD7Q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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3b74847e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(ds, ps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.9859002580259643,\n",
       " 0.0016500001729594493,\n",
       " 3.4241476040356815e-06,\n",
       " 8.241844493525158e-11,\n",
       " 1.905292725093347e-16,\n",
       " 1.486178276722453e-20,\n",
       " 1.1188126176620231e-24,\n",
       " 2.3593472535989218e-27,\n",
       " 7.841160843984151e-29,\n",
       " 1.907586245190865e-29]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats\n",
    "\n",
    "import statsmodels\n",
    "\n",
    "ps = []\n",
    "ds = []\n",
    "for d in np.linspace(0, 2, 11):\n",
    "    frac_df = frac_diff_FFD(dummy, d)\n",
    "    close_frac = frac_df[\"Close\"]\n",
    "    if len(close_frac) > 0:\n",
    "        # Coefficient of the first argument will change\n",
    "        res = stats.jarque_bera(close_frac)\n",
    "        ps.append(res[1])\n",
    "        ds.append(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f3b744ddc50>]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XokKUhrsKaQUloT14qSMDkxO447KhrCoopehEndXlqBCk4a5Cmr24KmSnHOjK4hkjSIiN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\nqwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3b745ccb38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(ds, ps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.0,\n",
       " 0.19256909554935164,\n",
       " 0.00382097956071048,\n",
       " 0.29614207494675404,\n",
       " 0.9670513001415704,\n",
       " 0.556062541284073,\n",
       " 0.26795184444641484,\n",
       " 0.19063509477251905,\n",
       " 0.18613335612111892,\n",
       " 0.21030382745120735]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5.6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from finance_ml.stats import get_daily_vol\n",
    "from finance_ml.labeling import cusum_filter, get_t1, get_events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ = df[[\"Close\", \"Open\", \"Low\", \"High\"]]\n",
    "frac_df = frac_diff_FFD(df_, 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "vol = get_daily_vol(close)\n",
    "events = cusum_filter(close, 2 * vol)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tomoaki/anaconda3/lib/python3.6/site-packages/finance_ml-0.1-py3.6.egg/finance_ml/labeling/barriers.py:37: FutureWarning: \n",
      "Passing list-likes to .loc or [] with any missing label will raise\n",
      "KeyError in the future, you can use .reindex() as an alternative.\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike\n",
      "2018-07-02 02:34:37.663817 100.0% apply_ptslt1 done after 0.0 minutes. Remaining 0.0 minutes..\n"
     ]
    }
   ],
   "source": [
    "t1 = get_t1(close, events, num_days=5)\n",
    "sampled = get_events(close, events, ptsl=2, trgt=vol, min_ret=0, num_threads=12,\n",
    "               t1=t1, side=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2004-08-26</th>\n",
       "      <td>2004-08-31</td>\n",
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       "      <th>2004-09-02</th>\n",
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       "      <th>2004-09-21</th>\n",
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       "      <th>2004-10-20</th>\n",
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       "      <th>2004-10-21</th>\n",
       "      <td>2004-10-22</td>\n",
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       "      <th>2004-10-25</th>\n",
       "      <td>2004-11-01</td>\n",
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       "    <tr>\n",
       "      <th>2004-10-26</th>\n",
       "      <td>2004-10-28</td>\n",
       "      <td>0.030639</td>\n",
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       "    <tr>\n",
       "      <th>2004-10-28</th>\n",
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       "      <th>2004-11-08</th>\n",
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       "      <th>2004-11-11</th>\n",
       "      <td>2004-11-16</td>\n",
       "      <td>0.032347</td>\n",
       "      <td>t1</td>\n",
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       "    <tr>\n",
       "      <th>2004-11-16</th>\n",
       "      <td>2004-11-22</td>\n",
       "      <td>0.034198</td>\n",
       "      <td>t1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-12-06</th>\n",
       "      <td>2004-12-13</td>\n",
       "      <td>0.031122</td>\n",
       "      <td>t1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-12-14</th>\n",
       "      <td>2004-12-20</td>\n",
       "      <td>0.030572</td>\n",
       "      <td>t1</td>\n",
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       "    <tr>\n",
       "      <th>2004-12-16</th>\n",
       "      <td>2004-12-21</td>\n",
       "      <td>0.029990</td>\n",
       "      <td>t1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-03</th>\n",
       "      <td>2005-01-06</td>\n",
       "      <td>0.028472</td>\n",
       "      <td>sl</td>\n",
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       "    <tr>\n",
       "      <th>2005-01-04</th>\n",
       "      <td>2005-01-10</td>\n",
       "      <td>0.029107</td>\n",
       "      <td>t1</td>\n",
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       "    <tr>\n",
       "      <th>2005-01-19</th>\n",
       "      <td>2005-01-24</td>\n",
       "      <td>0.027555</td>\n",
       "      <td>sl</td>\n",
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       "    <tr>\n",
       "      <th>2005-01-26</th>\n",
       "      <td>2005-01-31</td>\n",
       "      <td>0.029303</td>\n",
       "      <td>t1</td>\n",
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       "    <tr>\n",
       "      <th>2005-01-27</th>\n",
       "      <td>2005-02-01</td>\n",
       "      <td>0.028987</td>\n",
       "      <td>t1</td>\n",
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       "    <tr>\n",
       "      <th>2005-02-02</th>\n",
       "      <td>2005-02-07</td>\n",
       "      <td>0.030356</td>\n",
       "      <td>t1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-02-14</th>\n",
       "      <td>2005-02-22</td>\n",
       "      <td>0.030274</td>\n",
       "      <td>t1</td>\n",
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       "    <tr>\n",
       "      <th>2005-02-22</th>\n",
       "      <td>2005-02-28</td>\n",
       "      <td>0.029531</td>\n",
       "      <td>t1</td>\n",
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       "    <tr>\n",
       "      <th>2005-03-17</th>\n",
       "      <td>2005-03-22</td>\n",
       "      <td>0.026702</td>\n",
       "      <td>t1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-04-19</th>\n",
       "      <td>2005-04-21</td>\n",
       "      <td>0.023237</td>\n",
       "      <td>pt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-04-26</th>\n",
       "      <td>2005-05-02</td>\n",
       "      <td>0.023831</td>\n",
       "      <td>t1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-05-18</th>\n",
       "      <td>2005-05-23</td>\n",
       "      <td>0.021412</td>\n",
       "      <td>pt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <th>2016-06-02</th>\n",
       "      <td>2016-06-07</td>\n",
       "      <td>0.012707</td>\n",
       "      <td>t1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-08</th>\n",
       "      <td>2016-06-13</td>\n",
       "      <td>0.012583</td>\n",
       "      <td>t1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-20</th>\n",
       "      <td>2016-06-24</td>\n",
       "      <td>0.011971</td>\n",
       "      <td>sl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-27</th>\n",
       "      <td>2016-06-30</td>\n",
       "      <td>0.011577</td>\n",
       "      <td>pt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-18</th>\n",
       "      <td>2016-07-25</td>\n",
       "      <td>0.011406</td>\n",
       "      <td>t1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-19</th>\n",
       "      <td>2016-07-25</td>\n",
       "      <td>0.011294</td>\n",
       "      <td>t1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-02</th>\n",
       "      <td>2016-08-08</td>\n",
       "      <td>0.010604</td>\n",
       "      <td>t1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-06</th>\n",
       "      <td>2016-09-09</td>\n",
       "      <td>0.009207</td>\n",
       "      <td>sl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-08</th>\n",
       "      <td>2016-09-13</td>\n",
       "      <td>0.009090</td>\n",
       "      <td>t1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-12</th>\n",
       "      <td>2016-09-19</td>\n",
       "      <td>0.009155</td>\n",
       "      <td>t1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-13</th>\n",
       "      <td>2016-09-19</td>\n",
       "      <td>0.009273</td>\n",
       "      <td>t1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-15</th>\n",
       "      <td>2016-09-20</td>\n",
       "      <td>0.009259</td>\n",
       "      <td>t1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-26</th>\n",
       "      <td>2016-10-03</td>\n",
       "      <td>0.009335</td>\n",
       "      <td>t1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-27</th>\n",
       "      <td>2016-10-03</td>\n",
       "      <td>0.009323</td>\n",
       "      <td>t1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-29</th>\n",
       "      <td>2016-10-04</td>\n",
       "      <td>0.009264</td>\n",
       "      <td>t1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-10</th>\n",
       "      <td>2016-10-17</td>\n",
       "      <td>0.009131</td>\n",
       "      <td>t1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-11</th>\n",
       "      <td>2016-10-17</td>\n",
       "      <td>0.009094</td>\n",
       "      <td>t1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-18</th>\n",
       "      <td>2016-10-24</td>\n",
       "      <td>0.009182</td>\n",
       "      <td>t1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-20</th>\n",
       "      <td>2016-10-25</td>\n",
       "      <td>0.009103</td>\n",
       "      <td>t1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-24</th>\n",
       "      <td>2016-10-27</td>\n",
       "      <td>0.009187</td>\n",
       "      <td>sl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-25</th>\n",
       "      <td>2016-10-31</td>\n",
       "      <td>0.009209</td>\n",
       "      <td>sl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-02</th>\n",
       "      <td>2016-11-07</td>\n",
       "      <td>0.009622</td>\n",
       "      <td>t1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-07</th>\n",
       "      <td>2016-11-10</td>\n",
       "      <td>0.010206</td>\n",
       "      <td>sl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-09</th>\n",
       "      <td>2016-11-10</td>\n",
       "      <td>0.010209</td>\n",
       "      <td>sl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-10</th>\n",
       "      <td>2016-11-14</td>\n",
       "      <td>0.011065</td>\n",
       "      <td>sl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-15</th>\n",
       "      <td>2016-11-21</td>\n",
       "      <td>0.012078</td>\n",
       "      <td>t1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-21</th>\n",
       "      <td>2016-11-28</td>\n",
       "      <td>0.011895</td>\n",
       "      <td>t1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-30</th>\n",
       "      <td>2016-12-05</td>\n",
       "      <td>0.011646</td>\n",
       "      <td>t1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-05</th>\n",
       "      <td>2016-12-09</td>\n",
       "      <td>0.011873</td>\n",
       "      <td>pt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-03</th>\n",
       "      <td>2017-01-09</td>\n",
       "      <td>0.010982</td>\n",
       "      <td>pt</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>566 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   t1      trgt t1_type\n",
       "2004-08-26 2004-08-31  0.027366      t1\n",
       "2004-09-02 2004-09-07  0.024097      t1\n",
       "2004-09-21 2004-09-27  0.020498      t1\n",
       "2004-09-28 2004-10-04  0.024991      pt\n",
       "2004-09-30 2004-10-05  0.024733      pt\n",
       "2004-10-18 2004-10-20  0.022150      sl\n",
       "2004-10-20 2004-10-21  0.024583      pt\n",
       "2004-10-21 2004-10-22  0.026541      pt\n",
       "2004-10-25 2004-11-01  0.030133      t1\n",
       "2004-10-26 2004-10-28  0.030639      pt\n",
       "2004-10-28 2004-11-02  0.030077      t1\n",
       "2004-11-04 2004-11-05  0.030073      sl\n",
       "2004-11-08 2004-11-11  0.029604      pt\n",
       "2004-11-11 2004-11-16  0.032347      t1\n",
       "2004-11-16 2004-11-22  0.034198      t1\n",
       "2004-12-06 2004-12-13  0.031122      t1\n",
       "2004-12-14 2004-12-20  0.030572      t1\n",
       "2004-12-16 2004-12-21  0.029990      t1\n",
       "2005-01-03 2005-01-06  0.028472      sl\n",
       "2005-01-04 2005-01-10  0.029107      t1\n",
       "2005-01-19 2005-01-24  0.027555      sl\n",
       "2005-01-26 2005-01-31  0.029303      t1\n",
       "2005-01-27 2005-02-01  0.028987      t1\n",
       "2005-02-02 2005-02-07  0.030356      t1\n",
       "2005-02-14 2005-02-22  0.030274      t1\n",
       "2005-02-22 2005-02-28  0.029531      t1\n",
       "2005-03-17 2005-03-22  0.026702      t1\n",
       "2005-04-19 2005-04-21  0.023237      pt\n",
       "2005-04-26 2005-05-02  0.023831      t1\n",
       "2005-05-18 2005-05-23  0.021412      pt\n",
       "...               ...       ...     ...\n",
       "2016-06-02 2016-06-07  0.012707      t1\n",
       "2016-06-08 2016-06-13  0.012583      t1\n",
       "2016-06-20 2016-06-24  0.011971      sl\n",
       "2016-06-27 2016-06-30  0.011577      pt\n",
       "2016-07-18 2016-07-25  0.011406      t1\n",
       "2016-07-19 2016-07-25  0.011294      t1\n",
       "2016-08-02 2016-08-08  0.010604      t1\n",
       "2016-09-06 2016-09-09  0.009207      sl\n",
       "2016-09-08 2016-09-13  0.009090      t1\n",
       "2016-09-12 2016-09-19  0.009155      t1\n",
       "2016-09-13 2016-09-19  0.009273      t1\n",
       "2016-09-15 2016-09-20  0.009259      t1\n",
       "2016-09-26 2016-10-03  0.009335      t1\n",
       "2016-09-27 2016-10-03  0.009323      t1\n",
       "2016-09-29 2016-10-04  0.009264      t1\n",
       "2016-10-10 2016-10-17  0.009131      t1\n",
       "2016-10-11 2016-10-17  0.009094      t1\n",
       "2016-10-18 2016-10-24  0.009182      t1\n",
       "2016-10-20 2016-10-25  0.009103      t1\n",
       "2016-10-24 2016-10-27  0.009187      sl\n",
       "2016-10-25 2016-10-31  0.009209      sl\n",
       "2016-11-02 2016-11-07  0.009622      t1\n",
       "2016-11-07 2016-11-10  0.010206      sl\n",
       "2016-11-09 2016-11-10  0.010209      sl\n",
       "2016-11-10 2016-11-14  0.011065      sl\n",
       "2016-11-15 2016-11-21  0.012078      t1\n",
       "2016-11-21 2016-11-28  0.011895      t1\n",
       "2016-11-30 2016-12-05  0.011646      t1\n",
       "2016-12-05 2016-12-09  0.011873      pt\n",
       "2017-01-03 2017-01-09  0.010982      pt\n",
       "\n",
       "[566 rows x 3 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = sampled.dropna()\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tomoaki/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: FutureWarning: \n",
      "Passing list-likes to .loc or [] with any missing label will raise\n",
      "KeyError in the future, you can use .reindex() as an alternative.\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "features_df = frac_df.loc[data.index].dropna()\n",
    "features = features_df.values\n",
    "label = data['t1_type'].loc[features_df.index].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "clf = RandomForestClassifier()\n",
    "clf.fit(features, label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['t1', 't1', 't1', 't1', 't1', 't1', 't1', 't1', 'sl', 'pt', 't1',\n",
       "       't1', 't1', 'pt', 'pt', 't1', 'sl', 'pt', 't1', 'sl', 'pt', 't1',\n",
       "       'sl', 'sl', 'pt', 'sl', 'sl', 'sl', 't1', 't1', 'sl', 't1', 't1',\n",
       "       't1', 'pt', 't1', 'sl', 'sl', 'sl', 'pt', 'pt', 't1', 't1', 't1',\n",
       "       't1', 't1', 't1', 't1', 't1', 't1', 't1', 'sl', 't1', 'pt', 't1',\n",
       "       't1', 't1', 'pt', 't1', 't1', 'pt', 't1', 't1', 't1', 'pt', 't1',\n",
       "       't1', 't1', 'pt', 't1', 't1', 't1', 't1', 'sl', 't1', 't1', 't1',\n",
       "       't1', 'pt', 't1', 't1', 't1', 'sl', 'sl', 't1', 't1', 'pt', 'sl',\n",
       "       'pt', 't1', 't1', 't1', 't1', 't1', 't1', 't1', 'pt', 't1', 't1',\n",
       "       'sl', 'pt', 'pt', 't1', 't1', 'sl', 'sl', 't1', 't1', 'pt', 'pt',\n",
       "       't1', 't1', 't1', 't1', 't1', 't1', 't1', 'sl', 'pt', 't1', 'sl',\n",
       "       'sl', 'sl', 't1', 't1', 't1', 't1', 't1', 'sl', 'pt', 't1', 't1',\n",
       "       't1', 't1', 't1', 't1', 't1', 'pt', 't1', 't1', 't1', 't1', 'sl',\n",
       "       'pt', 't1', 'pt', 'pt', 'pt', 'pt', 'pt', 't1', 'sl', 'sl', 'sl',\n",
       "       'sl', 'pt', 'sl', 't1', 'sl', 'sl', 'pt', 'pt', 'pt', 't1', 't1',\n",
       "       'pt', 'pt', 'pt', 't1', 't1', 'pt', 't1', 't1', 't1', 'pt', 'pt',\n",
       "       't1', 'sl', 'sl', 't1', 'sl', 't1', 't1', 't1', 't1', 'sl', 't1',\n",
       "       't1', 't1', 't1', 'pt', 'sl', 'sl', 't1', 'sl', 'pt', 't1', 't1',\n",
       "       't1', 't1', 'pt', 't1', 'pt', 'pt', 't1', 'pt', 't1', 'pt', 't1',\n",
       "       't1', 't1', 't1', 't1', 't1', 't1', 'sl', 't1', 't1', 't1', 't1',\n",
       "       't1', 'sl', 't1', 't1', 'pt', 't1', 't1', 't1', 't1', 'sl', 'sl',\n",
       "       't1', 't1', 'sl', 't1', 't1', 'pt', 'pt', 't1', 't1', 't1', 't1',\n",
       "       't1', 't1', 't1', 't1', 'sl', 'sl', 't1', 't1', 't1', 't1', 't1',\n",
       "       'pt', 't1', 't1', 't1', 'sl', 'sl', 't1', 'pt', 'pt', 't1', 't1',\n",
       "       't1', 't1', 't1', 't1', 'pt', 't1', 't1', 't1', 't1', 't1', 'pt',\n",
       "       'pt', 'pt', 'pt', 'pt', 't1', 't1', 'sl', 'sl', 't1', 't1', 't1',\n",
       "       'pt', 't1', 'sl', 'pt', 't1', 't1', 't1', 't1', 'sl', 't1', 'pt',\n",
       "       't1', 't1', 't1', 't1', 't1', 'pt', 'sl', 't1', 'pt', 'pt', 'sl',\n",
       "       'sl', 't1', 't1', 't1', 't1', 'sl', 'pt', 't1', 'sl', 't1', 't1',\n",
       "       'pt', 'pt', 'pt', 't1', 't1', 't1', 't1', 't1', 't1', 't1', 't1',\n",
       "       'pt', 'sl', 't1', 't1', 'pt', 'pt', 'pt', 'sl', 't1', 't1', 'sl',\n",
       "       'sl', 'pt', 'sl', 'sl', 'sl', 't1', 't1', 't1', 't1', 't1', 'sl',\n",
       "       'pt', 't1', 't1', 't1', 'sl', 't1', 't1', 't1', 't1', 't1', 't1',\n",
       "       't1', 't1', 't1', 't1', 't1', 'sl', 'sl', 't1', 'sl', 'sl', 'sl',\n",
       "       't1', 't1', 't1', 'pt', 'pt'], dtype=object)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.predict(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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